The engineering of IoT systems brings about various challenges due to the inherent complexities associated with such heterogeneous systems. In this paper, we propose a library of statechart templates, STL4IoT, for designing complex IoT systems. We have developed atomic statechart components modeling the heterogeneous aspects of IoT systems including sensors, actuators, physical entities, network, and controller. Base system units for smart systems have also been designed. A component for calculating power usage is available in the library. In addition, a smart hub template that controls interactions among multiple IoT systems and manages power consumption has also been proposed. The templates aim to facilitate the modeling and simulation of IoT systems. Our work is demonstrated with a smart home system consisting of a smart hub of lights, a smart microwave, a smart TV, and a smart fire alarm system. We have created a multi statechart with Itemis CREATE based on the proposed templates and components. A smart home simulator has been developed by generating controller code from the statechart and integrating it with a user interface.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11875881PMC
http://dx.doi.org/10.1177/00375497241290369DOI Listing

Publication Analysis

Top Keywords

iot systems
20
smart
8
smart hub
8
proposed templates
8
systems
7
iot
6
stl4iot statechart
4
statechart template
4
template library
4
library iot
4

Similar Publications

The volume of confidential information transmitted over 5G networks has increased rapidly due to the widespread adoption of large machine-type communication and Internet of Things (IoT) devices. Secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) parameters are crucial parameters used in evaluating the security of wireless systems, particularly in situations where maintaining secrecy is essential. Also, Non-orthogonal multiple access (NOMA) has the potential to improve the performance of wireless communication systems due to its higher spectral efficiency, improved fairness in resource allocation, and enhanced coverage and connectivity.

View Article and Find Full Text PDF

The rapid growth of the Internet of Things (IoT) and its extensive use in many regions, such as smart homes, healthcare, and vehicles, have made IoT security increasingly critical. Ransomware is an advanced and adjustable threat influencing users globally, limiting admittance to their data or systems over models like file encryption or screen locking. Traditional ransomware detection methods frequently drop, deprived of the ability to combat these threats successfully.

View Article and Find Full Text PDF

I-BrainNet: Deep Learning and Internet of Things (DL/IoT)-Based Framework for the Classification of Brain Tumor.

J Imaging Inform Med

March 2025

Artificial Intelligence, Software, Information Systems Engineering Departments, AI and Robotics Institute, Near East University, Mersin10, Nicosia, Turkey.

Brain tumor is categorized as one of the most fatal form of cancer due to its location and difficulty in terms of diagnostics. Medical expert relies on two key approaches which include biopsy and MRI. However, these techniques have several setbacks which include the need of medical experts, inaccuracy, miss-diagnosis as a result of anxiety or workload which may lead to patient morbidity and mortality.

View Article and Find Full Text PDF

In recent years, growth in technology has significantly impacted various industries, including sports, health, e-commerce, and agriculture. Among these industries, the sports sector is experiencing significant transformation, which needs support in accurately monitoring athlete predicting and performance injuries arising due to traditional methods' limitations. Keeping the above in mind, in this article, we present the Intelligent Sports Management System (ISMS) with the integration of wireless sensor networks (WSNs) and neural networks (NNs), which enhance athlete monitoring and injury prediction.

View Article and Find Full Text PDF

Over the past two decades, sequential recommendation systems have garnered significant research interest, driven by their potential applications in personalized product recommendations. In this article, we seek to explicitly model an algorithm based on Internet of Things (IoT) data to predict the next cell reached by the user equipment (UE). This algorithm exploits UE embedding and cell embedding combining the visit time interval information, and uses sliding window sampling to process more UE trajectory data.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!